Activity report
RNSR: 202224287H
In partnership with:
INSERM, Université Paul-Valéry Montpellier 3, Université de Montpellier
Team name:
Precision Medicine by Data Integration and Causal Learning
In collaboration with:
Institut Desbrest d’Épidémiologie et de Santé Publique (IDESP)
Digital Health, Biology and Earth
Computational Neuroscience and Medicine
Creation of the Project-Team: 2022 June 01


Computer Science and Digital Science

  • A6.1. Methods in mathematical modeling
  • A9.2. Machine learning
  • A9.6. Decision support

Other Research Topics and Application Domains

  • B2. Health
  • B2.2. Physiology and diseases
  • B2.3. Epidemiology

1 Team members, visitors, external collaborators

Research Scientist

  • Julie Josse [Team leader, INRIA, Senior Researcher, HDR]

Faculty Members

  • Pascal Demoly [UNIV MONTPELLIER, Professor, from Jun 2022]
  • Pierre Lafaye De Micheaux [UNIV MONTPELLIER III, Associate Professor, from Jun 2022]
  • Nicolas Molinari [UNIV MONTPELLIER, Professor, from Jun 2022]

PhD Students

  • Marie Felicia Beclin [UNIV MONTPELLIER, from Jun 2022]
  • Benedicte Colnet [inria]
  • Maxime Fosset [UNIV MONTPELLIER, from Jun 2022]
  • Margaux Zaffran [EDF, CIFRE]

Administrative Assistant

  • Annie Aliaga [INRIA]

2 Overall objectives

The objective of the PreMeDICaL team (Precision Medicine by Data Integration and Causal Learning) is to develop the next generation of methods/algorithms to extract knowledge from health data and improve the care of patients. More specifically, the aim is to develop learning tools for personalized treatment effect prediction and for predicting outcome, while integrating different data sources to guide decisions made by clinicians and authorities. PreMeDICaL has two research axes:

  1. Personalized medicine by optimal prescription of treatment. We will develop causal inference techniques for (dynamic) policy learning (allocating the best treatment for each person at the right time), that handle missing values and leverage both RCTs and observational data. Using both data sources allow to better design future RCTs or to launch a drug without running RCTs and in the longer term to rethink the evidence needed to bring treatments to the market and to do so more quickly.
  2. Personalized medicine by integration of different data sources. We will build predictive models for heterogeneous data: for instance given monitoring data in continuous time, images and clinical data what is the risk for an event to occur? Is it useful to have all the sources or do they provide the same information? We will additionally develop solutions to handle missing values in a supervised learning setting and to improve the confidence of the outputs of the predictive models.

The aim is to push methodological innovation up to the stakeholders (patients, clinicians, regulators, etc.). Consequently, beyond these methodological developments, innovative responses to the public health challenge posed by respiratory allergies are targeted. In addition to leveraging machine learning algorithms and leveraging appropriate data, combining them with clinical expertise and existing recommendations is necessary. Long- term aims are to have both a strong scientific and societal impact with a substantial impact on the quality of care for patients and major consequences for the medical profession by providing a much earlier access to innovative solutions and more efficient treatment and care. With a successful proof of concept in the domain of allergies, by having clear reproducible pipelines, methodologies, software (by providing clinical decision making system tools) we could thereafter consider other pathologies (such as traumatology and oncology studied at IDESP). Hence, a joint team between Inria and Inserm provides a unique opportunity for trans-disciplinary research and collaboration bringing together mathematical, methodological, technological and medical expertise. The PreMeDICaL team contributes to precision medicine (where the treatment/device is adapted on a patient basis) and to translational medicine which aims at bridging the gap between fundamental research and its practical use.

3 Research program

3.1 Research Axis 1: Personalized medicine by optimal prescription of treatment

Randomized controlled trials are considered the gold standard approach for assessing the causal effect (i.e., the treatment effect) of an intervention or a treatment on an outcome of interest. Indeed, the allocation of the treatment is under control, which implies that there is no confounding factors (the distribution of covariates for treated and control patients is asymptotically balanced) that could interfere with the treatment and simple estimators (such as the difference in mean effect between the treated and controls) can be used to consistently estimate the average treatment effect (ATE). However, RCTs can come with drawbacks. They can be expensive, take a long time to set up, and be compromised by insufficient sample size due to either recruitment difficulties or restrictive inclusion/exclusion criteria. These criteria can lead to a narrowly defined trial sample that differs markedly from the population potentially eligible for the treatment (distributional shift). Therefore, the findings from RCTs can lack generalizability (or external validity). This has been largely published in the field of respiratory and allergic diseases, see for instance (Pahus et al, 2019) which highlights that the population from RCTs represents less than 10% of the population that will receive treatments.

In contrast, there is an abundance of observational data, collected without systematically designed interventions. Such data can come from different sources: they can be collected from research sources (such as disease registries, cohorts, biobanks, epidemiological studies), or they can be routinely collected (through electronic health records, insurance claims, administrative databases, patients' App, etc). In that sense, observational data can be readily available, can include large samples representative of the target populations, and can be less costly than RCTs. To leverage observational data for treatment effect estimation in health domains, several laws built on studies by the USA Food and Drug Administration (FDA) encourage the use of “real world data” (RWD), defined as data “derived from sources other than randomized clinical trials”, for regulatory decision making. Clinical evidence regarding the usage and potential benefits or risks of a medical product derived from the analysis of RWD is named Real World Evidence (RWE). The European Medicines Agency (EMA) is also a very active regulatory authority working with RWD to facilitate development and access to medicines. However, despite the large number of methods available to estimate the causal treatment effect from observational data such as matching, inverse probability weighting (IPW) or more recent doubly robust methods based on machine learning there are often concerns about the quality of these “big data” and causal claims. Indeed, building on observational data is still not consensual due to the lack of controlled experimental interventions, which opens the door to confounding biases (lack of internal validity).

Observational data and clinical trial data can provide different perspectives when evaluating an intervention or a medical treatment. Combining the information gathered from experimental and observational data is a promising avenue for medical research, because the knowledge acquired from integrative analyses could not be gathered from a single-source analysis alone. Three potential high impact applications of observational and clinical data are:

  1. Predicting the effect of a treatment estimated on a RCT, on a new target population (generalization);
  2. Comparing RCTs and RWE to validate observational methods;
  3. Better estimation of heterogeneous treatment effects.

There is an abundant literature on bridging the findings from an RCT to a target population and combining both sources of information. Similar problems have been termed as transportability, and data fusion and have connections to the covariate shift/domain generalization problem in ML. Colnet et al, 2020. reviewed the methods to generalize the treatment effect while integrating the distributional shift (IPSW, g-formula, AIPSW, calibration weighting, etc.) (a), or improve the estimate of the conditional average treatment effect (CATE, i.e. heterogeneous effect) while correcting for confounding factors not measured in the observational study (c). However, these methods have many shortcomings and there are still many challenges to address. We provide below examples of methodological locks we will overcome.

  • Handling missing values and unmeasured covariates with multi-source data;
  • Transfert Learning of optimal individualized treatment regimes with right-censored survival data;
  • Policy learning and dynamic treatment policy with missing values.

3.2 Research axis 2: Personalized medicine by integration of different data sources

Integrating heterogeneous data (time series, images, text, numerical or categorical data) potentially from different centers to establish predictive models involves many obstacles. In the case where a patient is described by several sources, problems of high dimensionality are exacerbated. We will start by the question of the links between the sources before tackling some challenges posed by missing values:

  • Relationship between the different sources;
  • (Informative) missing values in time series and structured by blocks;
  • Conformal prediction with missing values;
  • Federative learning with missing values.

4 Application domains

The first application domain of PreMeDICaL is respiratory diseases and in particular Asthma. For more than 30 years, there has been an increase in a number of chronic non-communicable diseases (NCD), such as asthma and allergies, respiratory diseases. Allergies are the fourth most common chronic disease in the world. The World Health Organization (WHO) predicts that by 2050, one in two people in the world will suffer from allergies. In France, the number of people suffering from allergies has doubled in 20 years, particularly among children and young people. Although the expression of these diseases results from the interaction between the genetic background and the environment, especially through epigenetic mechanisms, their sudden increase is solely due to the environmental changes that occurred in the last decades because of the Western lifestyle, the genetic heritage requiring centuries to change. A full understanding of the complexity of chronic NCD prompts researchers to analyse large data utilizing proper markers and tools (e.g., biological, clinical, behavioural, economic, social, demographic, environmental data, patient experience, patient social networks) in an etiological and evaluative way to determine phenotypical patients’ pathways, explain their impacts, their causes, their influences, prevent them and improve their prognosis. Integrating these different sources of information, collected by several actors (healthcare professionals, public authorities or patients themselves), thus offer new opportunities to design personalized solutions by adapting treatment to the patient and the organizational context, leading to improved patient care and prevention policies.

With a successful proof of concept in the domain of allergies, by having clear reproducible pipelines, methodologies, software we will thereafter consider other pathologies (such as traumatology and oncology studied at IDESP).

5 Social and environmental responsibility

5.1 Impact of research results

From a methodological point of view, the aim is to improve and develop new statistical and ML methods for establishing evidence on the efficiency of treatment by data enrichment (data fusion), by taking the example of AIT in respiratory diseases. An important output of this research is that these methodological works have a concrete impact on designing future clinical trials and that the new methodology will be supported by regulatory authorities. Indeed, exploiting both RCTs and observational data serve different purposes such as prediction of the treatment effect on new populations, increasing the generalization of clinical trials (so that they are more representative of the patient population who may benefit from the treatment) and also defining new inclusion criteria (because we identify subgroups who can benefit from treatment). This research is part of the PEPR project "Next methodological challenges in clinical trials in the era of digital health".

From a technological point of view, the aim is to provide software (starting with open access) for these methods to be applied in practice by studies stakeholders, clinicians and the clinical trial community.

From the clinical and patients point of view, the different projects aim at quantifying the clinical benefit of treatment (over time), taking into account all patient characteristics, and provide useful clinical prognosis tools allowing clinicians to optimally treat every patient. The aim is to give patients better care and early access to innovation. In addition, these works can lead to a better adoption by the medical community of certain (advanced) techniques used to estimate the effects of treatment on patients (by comparing the results obtained in an RCT with the RWE).

From a public-health point of view, the aim is to guide decisions made by investigators, sponsors and authorities. Better trials’ designs may also have an important impact in terms of cost reduction. Finally, we aim at having a significant impact in the field of allergy treatments providing new knowledge that may change guidelines and practice.

6 Highlights of the year

6.1 Awards

Margaux Zaffran received the Séphora Berrebi Women in Advanced Mathematics & Computer Science Scholarship which aims at encouraging active involvment of young women in scientific research, especially in the mathematics and computer science areas. They recognized her PhD work on conformal prediction and the grant allowed her to spend a research stay at the Technion Israel.

6.2 Other highlights

  • Pascal Demoly renew the direction (with Luciana Kase Tanno) of the WHO Collaborating Center for “Scientific Support for Classifications” (2018-2022 and 2022-2026)
  • Pascal Demoly was elected President of the French Society of Allergology (2022-2024)
  • Pascal Demoly was appointed to the strategic and scientific orientation council of the recent ExposUM Institute (created in 2022 as part of the PIA4 Excellencies project)

7 New software and platforms

7.1 New software

7.1.1 factominer

  • Keywords:
    Dimensionality reduction, PCA, Text mining, Clustering
  • Functional Description:

    The FactoMineR package is dedicated to performing principal components methods to explore, sum­-up and visualize data. Dimensionality reduction methods include PCA, correspondence analysis (CA) for count data such as documents-­words data, multiple correspondence analysis (MCA) for categorical data such as survey data, factorial analysis of mixed data (FAMD) for both types of variables as well as methods for groups of variables, of individuals (multiple factorial analysis, MFA), for hierarchy …

    References: https://husson.github.io/MOOC_AnaDo/index.html https://husson.github.io/MOOC.html#PCAcourse

  • URL:
  • Contact:
    Julie Josse
  • Partner:

7.1.2 missMDA

  • Keyword:
    Missing data
  • Functional Description:
    The missMDA package is dedicated to missing values in and with Multivariate Data Analysis. It allows one to apply PCA, MCA, FAMD and MFA on incomplete data. It performs single and multiple imputation for continuous, categorical and mixed data based on principal components methods
  • URL:
  • Contact:
    Julie Josse
  • Partner:

7.2 New platforms

Causal inference taskview: to list and organize all the R packages on causal inference

Participants: Julie Josse, Pan Zhao.

8 New results

8.1 Generalization of clinical trials

Participants: Benedicte Colnet, Julie Josse.


RCTs are the current gold-standard to empirically measure a causal effect of a given intervention on an outcome. But more recently, concerns have been raised on the limited scope of RCTs: stringent eligibility criteria, unrealistic real-world compliance, short timeframe, limited sample size, etc. Such limitations threaten the external validity of RCT studies to other situations or populations 51. The usage of complementary non-randomized data, referred to as observational or from real world, brings promises as additional sources of evidence, in particular combined to trials. Transportability (also known as generalization, recoverability from sampling bias, or data-fusion 52, 49) allows to generalize or transport the trial findings toward a target population of interest, potentially subject to a covariate distributional shift.

Results: Reweighting the trial - Publications 11, 42

RCT and observational data are seldom acquired as part of a homogeneous effort. As a result, they come with different covariates. Restricting the analysis to the shared covariates raises the risk of omitting an important one leading to identifiabilities issues. This problem is reminiscent of unobserved confounding in causal inference with one observational data. In 1, we suggest a sensitivity analysis to handle cases where such covariates (namely treatment effect modifiers that are shifted between the two sets when studying risk difference) are missing in one or both sets. We also completed proofs on the consistency of generalization estimators that use either weighting (Inverse Propensity of Sampling Weighting, IPSW), outcome modeling , or combine the two in doubly robust approaches with Augmented IPSW (AIPSW). We further analysed the IPSW estimator, which consists of re-weighting the trial so that it resembles the observational sample, in 42. In particular, we established finite sample bias and variance (the literature mostly focuses on asymptotic results) and upper bound on the risk of different versions of the estimator: oracle, semi-oracle, etc. This work can lead to practical recommendations in terms of data collection (e.g., doubling the size of the observational data leads to a smaller asymptotic variance than doubling the size of the trial).

Results: Optimal policy for survival data

Participants: Pan Zhao, Julie Josse.

The optimal individualized treatment regime (ITR) learned from a source population, due to covariate shift, may not generalize well to the target population that we aim to apply the ITR on. We propose a transfer learning framework, where covariate information from the target population is available, for ITR estimation with heterogeneous populations and right-censored survival data, which is common in clinical studies and motivated by our medical application.

We characterize the efficient influence function (EIF) and propose a doubly robust estimator of the targeted value function, which accommodates a broad class of functionals of survival distributions. For a pre-specified class of ITRs, we establish the N-1/3 rate of convergence for the estimated parameter indexing the optimal ITR. Based on the Neyman orthogonality of the EIF, we also propose a cross-fitting procedure and show that the proposed optimal value estimator is consistent and asymptotically normal with flexible machine learning methods for nuisance parameter estimation.

8.2 Handling missing values

Results: Handling MNAR data in clustering

Participants: Julie Josse.

Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at random (MNAR). In this paper, we propose model-based clustering algorithms designed to handle very 5 general types of missing data, including MNAR data. To do so, we introduce a mixture model for different types of data (continuous, count, categorical and mixed) to jointly model the data distribution and the MNAR mechanism, remaining vigilant to the degrees of freedom of each. Eight different MNAR models which depend on the class membership and/or on the values of the missing variables themselves are proposed. For a particular type of MNAR models, for which 10 the missingness depends on the class membership, we show that the statistical inference can be carried out on the data matrix concatenated with the missing mask considering a MAR mechanism instead; this specifically underlines the versatility of the studied MNAR models. Then, we establish sufficient conditions for identifiability of parameters of both the data distribution and the mechanism. Regardless of the type of data and the mechanism, we propose to perform 15 clustering using EM or stochastic EM algorithms specially developed for the purpose. Finally, we assess the numerical performances of the proposed methods on synthetic data and on the real medical registry TraumaBase R as well.

8.3 Uncertainty quantification with conformal prediction

Participants: Julie Josse, Margaux Zaffran.


Most statistical learning and artificial intelligence methodologies provide point predictions, without any indication of the degree of confidence that can be given to these predictions (i.e. without predictive intervals). This lack of uncertainty quantification of predictive models is a major barrier to the adoption of powerful machine learning methods by society. Probabilistic forecasts, i.e. predicting the entire distribution probability and not only the conditional expectation, could partially tackle this issue but they are only valid asymptotically, require strong assumptions on the data (e.g. normality) or/and are model-dependent. The emergent field of conformal prediction (CP) 53, 48, 47 is a promising framework for distribution-free uncertainty quantification. It is a general procedure to build predictive intervals for any predictive model (including black-box methods such as deep learning), which are valid (i.e. achieve nominal marginal coverage), in finite sample, and without assumption on the data generation process except the exchangeability. This is extremely promising for decision support tools in critical applications: healthcare, autonomous driving, etc. An extension of CP (Conformalized Quantile Regression, 50) was used to predict the U.S. presidential elections (2020) by the Washington Post.

Results: Conformal prediction for time series - publication 3

Given the non-exchangeability of time series data, CP can not be applied as such to this framework. To achieve this task, we study and extend Adaptive Conformal Inference (ACI) 46 in the context of time series with general dependency. ACI is a method designed to handle an online setting, with distributional shift. It relies on using an adaptive miscoverage rate , that is updated according to previous performances and to an hyper-parameter, playing the role of a learning rate. First, we study theoretically, using Markov Chain theory, the impact of the learning rate on the length of the predictive intervals, in the exchangeable and auto-regressive case, in order to describe not only the validity but also the efficiency of ACI. This is hardly useful in practice: the optimal learning rate depends on the unknown data distribution. This is why we introduce AgACI, a parameter-free method using online expert aggregation 45. Finally, we compare ACI, AgACI and other methods slightly adapted to time series, on extensive synthetic experiments. These experiments highlight that AgACI achieves good performances in terms of validity and efficiency. To allow for better benchmarking of existing and new methods, we provide implementations in Python of (all) the described methods and a complete pipeline of analysis on GitHub.

Results: Conformal prediction with missing values

Uncertainty quantification has not been addressed with missing values. In the finite-sample regime, we show that, for almost all imputations and missing values mechanisms, the imputed data set is exchangeable. Thus, CP properties still hold and marginal guarantees are met. Nevertheless, we emphasize that the average coverage varies depending on the pattern of missing values: it tends to construct prediction intervals that often under-cover the response conditional on a given missing pattern. After theoretically studying the case of a linear model, we propose a methodology, missing data augmentation, to achieve approximate conditional guarantees conditional on the 2d patterns of missing values, where d is the data dimension.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

Participants: Nicolas Molinari, Pascal Demoly.

  • Participation to the Fondation TEZOS (Vigicard digital health card project) with the startup CodInsight
  • Co-creation of the startup AdviceMedica (collective intelligence for solving complex cases in medicine)
  • Title: Apprentissage et modélisation statistique en médecine du sommeil : du diagnostic au traitement
  • Company: groupe Adène
  • Duration: May 2020 - May 2023

9.2 Bilateral Grants with Industry

Participants: Julie Josse, Pascal Demoly.

  • Title: Combining RCT and observational data. Educational Grant
  • Company: Allergologisk Laboratorium Kobenhavn (ALK)
  • Duration: Sept 2022 - Sept 2023

Participants: Nicolas Molinari.

  • Title: Etude IRIS - Efficacité en vIe Réelle du dispositif de télésurveillance médicale Chronic Care ConnectTM chez les patients atteints d’InsuffiSance cardiaque chronique
  • Company: IQVIA
  • Duration: Oct 2022 - Oct 2023
  • Title: étude de vie réelle analysant la mortalité des patients BPCO sévères
  • Company: Sanofi
  • Duration: Nov 2022 - Dec 2023
  • Title: étude « Home-Care SIMEOX »,
  • Company: Agir à Dom
  • Duration: Nov 2019 - Nov 2023

10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Participation in other International Programs

International medical trials with AB science: member of the Data Safety Monitoring Board (DSMB) which will oversee the safety of study AB20001 entitled: “A Randomized, Open-label Phase 2 Clinical Trial to Evaluate the Safety and Efficacy of Masitinib combined with Isoquercetin, and Best Supportive Care in Hospitalized Patients with Moderate and Severe COVID-19.”

Participants: Nicolas Molinari.

10.2 International research visitors

10.2.1 Visits of international scientists

Other international visits to the team
Mats Julius Stensrud
  • Status
    researcher (Ass. Professor)
  • Institution of origin:
  • Country:
  • Dates:
    June 1
  • Context of the visit:
    Work on dynamic treatment regime
  • Mobility program/type of mobility:
    research stay
Michael Elliott
  • Status
    researcher (Professor)
  • Institution of origin:
    University of Michigan
  • Country:
  • Dates:
    June 3
  • Context of the visit:
    Talk on combining experimental and non experimental data
  • Mobility program/type of mobility:

10.2.2 Visits to international teams

Research stays abroad
  • 3 months visit at Departments of Electrical Engineering and of Computer Science at the Technion—Israel Institute of Technology to work with Yanniv Romano on conformal prediction - Margaux Zaffran
  • 3 months visit at the Statistic Department of Stanford University hosted by Trevor Hastie - Bénédicte Colnet

10.3 European initiatives

10.3.1 Horizon Europe

HORIZONHLTH-2021-ENVHLTH-02 SynAir-G (500k€)

Participants: Pascal Demoly.

10.3.2 Other european programs/initiatives

  • Participation to the consortium ECRHS (cohort European Community Respiratoiry Health Survey) with 2 ANR
  • Participation to the ENDA (European Network for Drug Allergy)

Participants: Pascal Demoly.

11 Dissemination

Dissemination from the creation of the PreMeDICaL team in June 2022 to December 2022

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

General chair, scientific chair

11.1.2 Scientific events: selection

Chair of conference program committees
Member of the conference program committees
  • Journée de la société Française de Statistique, Bruxelles, Belgium 2023 - Julie Josse
  • Statlearn, Montpellier, France, April 2023 - Julie Josse
  • IMS International Conference on Data Science, Florence, Italy, December 2022 - Julie Josse
  • Mathematical Methods of Modern Statistics 3. Luminy, France, June 2022 - Julie Josse

11.1.3 Journal

Member of the editorial boards
  • Journal of Statistical Software - Pierre Lafaye de Micheaux
  • Journal allergy clin immunology in practice, Allergy Asthma and Clin Immunol, Rev fr d’Allergologie - Pascal Demoly
  • European Respiratory Journal - Nicolas Molinari
Reviewer - reviewing activities

Annals of Statistics, Journal of Statistical Computations and Simulations, Journal of Statistical Software, WAO Journal, Allergy, Clin Exp Allergy

11.1.4 Invited talks

  • Online Causal Inference Seminar, Nov., 2022 - Julie Josse
  • Celebrating causal inference in medicine and public health, Oct. 2022, Ghent University, Belgium (before the Rousseeuw prize) - Julie Josse
  • Exposome Symposium, Oct. 2022, Montpellier, France - Julie Josse
  • AutoML conference (with ICML conference), Online, July 2022, Baltimore, USA - Julie Josse
  • IMS 2022, Talk in session on Modern Approaches to Missing Data. Online, June 2022, London, UK - Julie Josse
  • International Meeting on Statistical Methods in Biopharmacy, Sep. 2022 - Benedicte Colnet
  • Traumabase scientific comitee and Capgemini, Dec. 2022 - Benedicte Colnet
  • Causal Tau team, Oct. 2022 - Benedicte Colnet
  • Yaniv Romano’s Group Meeting, Haifa, Israel, July 2022 - Margaux Zaffran
  • Mathematical Methods of Modern Statistics 3, Marseille, France, July 2022 - Margaux Zaffran
  • Journées de statistiques, Lyon, France, June 2022 - Margaux Zaffran
  • Journées de statistiques, Lyon, France, June 2022 - Pan Zhao

11.1.5 Leadership within the scientific community

  • Traumatrix project: Since 2016, Julie Josse coordinate the Traumatrix project in partnership with the École Polytechnique (IPP), the CNRS, EHESS (École des Hautes Études en Sciences Sociales), Inria, the traumabase group (30 hospitals) and APHP (les hopitaux de Paris) to improve the management of polytrauma patients by leveraging observational data. The project is funded by ARS (Agence Regional de Santé) and by the skill sponsorship of Capgemini Invent (10 data scientists, project managers, data managers). The project is mature and we have launched prospective studies with a mobile application in the ambulances that collects data and predicts the risk of the patient going into hemorrhagic shock.
  • Vice-president of the Young Statisticians Group of the French Statistical Society - Margaux Zaffran

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

  • Master: ESEEC, 19.5eqTD, Les outils statistiques du diagnostic (modélisation et statistiques économiques et sociales), Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Master: ESEEC, 17.5eqTD, Analyse de Données Multidimensionnelles SHS, Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Master: MIASHS, 22.5eqTD, Statistique et probabilités bivariées, Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Bachelor: MIASHS, 26eqTD, Science des données 2, Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Master: MIASHS, head of projects of " Travaux d'Études et de Recherche (TER)", Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Master: MIASHS, head of "Marathon du Web", Université Paul Valery, Montpellier - Pierre Lafaye De Micheaux
  • Master: Institut de formation en masso-kinésithérapie, 9eqTD, statistics, Montpellier - Nicolas Molinari
  • Master: Institut de formation en masso-kinésithérapie, head of the program, statistics, Montpellier - Nicolas Molinari
  • Master: EDSB « Epidémiologie, Données de Santé, Biostatistique », head of « Grands enjeux en santé » , Université de Montpellier - Pascal Demoly

11.2.2 Supervision

  • PhD in progress: Marie Felicia Beclin - Pierre Lafaye De Micheaux and Nicolas Molinari
  • PhD in progress: Maxime Fosset - Julie Josse and Nicolas Molinari
  • PhD in progress: Margaux Zaffran - Julie Josse
  • PhD in progress: Pan Zhao - Julie Josse
  • Master in progress: Mame Fatou Gueye - Pierre Lafaye De Micheaux

11.2.3 Juries

  • PhD [with report] Elise Dumas, France - Julie Josse
  • Comité de Sélection to hire two permanent researchers "Chargé de Recherche" (Inrae), Apprentissage statistique pour les sciences du vivant et de l’environnement - Julie Josse

11.3 Popularization

11.3.1 Internal or external Inria responsibilities

  • Member of "Comité du suivi doctoral" Inria Sophia, to allocate PhD grants extensions, etc - Julie Josse
  • Member of the board for Region Occitanie to allocate PhD/postdoc grants in AI for health - Julie Josse

11.3.2 Articles and contents

11.3.3 Interventions

  • Women in Machine Learning profile highlight invitation - Julie Josse

12 Scientific production

12.1 Major publications

12.2 Publications of the year

International journals

  • 4 articleA.Alvar Agustí, E.Eleni Rapsomaniki, R.Richard Beasley, R.Rod Hughes, H.Hana Müllerová, A.Alberto Papi, I.Ian Pavord, M.Maarten van den Berge, R.Rosa Faner, G.Gabriel Benhabib, X. B.Xavier Bocca Ruiz, R.Ricardo del Olmo, R. E.Raul Eduardo Lisanti, G.Gustavo Marino, W.Walter Mattarucco, J.Juan Nogueira, M.Maria Parody, P.Pablo Pascale, P.Pablo Rodriguez, D.Damian Silva, G.Graciela Svetliza, C.Carlos Victorio, R. W.Roxana Willigs Rolon, A.Anahi Yañez, G.Gary Anderson, S.Stuart Baines, S.Simon Bowler, P.Peter Bremner, S.Sheetal Bull, P.Patrick Carroll, M.Mariam Chaalan, C.Claude Farah, G.Gary Hammerschlag, K.Kerry Hancock, Z.Zinta Harrington, G.Gregory Katsoulotos, J.Joshua Kim, D.David Langton, D.Donald Lee, M.Matthew Peters, L.Lakshman Prassad, H.Helen Reddel, D.Dimitar Sajkov, F.Francis Santiago, F. G.Frederick Graham Simpson, S.Sze Tai, P.Paul Thomas, P.Peter Wark, J. E.José Eduardo Delfini Cançado, T.Thúlio Cunha, M.Marina Lima, A.Alexandre Pinto Cardoso, M.Marcelo Rabahi, S.Syed Anees, J.John Bertley, A.Alan Bell, A.Amarjit Cheema, G.Guy Chouinard, M.Michael Csanadi, A.Anil Dhar, R.Ripple Dhillon, J. M.J. Mark Fitzgerald, D.David Kanawaty, A.Allan Kelly, W.William Killorn, D.Daniel Landry, R.Robert Luton, P.Piushkumar Mandhane, A.Andrew Mcivor, B.Bonavuth Pek, R.Robert Petrella, M.Mohsen Sadatsafavi, D.Daniel Stollery, M.Meihua Chen, Y.Yan Chen, W.Wei Gu, K. M.Kim Ming Christopher Hui, M.Manxiang Li, S.Shiyue Li, M.Ma Lijun, G.Guangyue Qin, W.Weidong Song, W.Wei Tan, Y.Yijun Tang, C.Chen Wang, T.Tan Wang, F.Fuqiang Wen, F.Feng Wu, P. C.Ping Chao Xiang, Z.Zuke Xiao, S.Shengdao Xiong, J.Jinghua Yang, J.Jingping Yang, C.Caiqing Zhang, M.Min Zhang, P.Ping Zhang, W.Wei Zhang, X.Xiaohe Zheng, D.Dan Zhu, F. B.Fabio Bolivar Grimaldos, A. C.Alejandra Cañas Arboleda, C. M.Carlos Matiz Bueno, D. M.Dora Molina de Salazar, E.Elisabeth Bendstrup, O.Ole Hilberg, C.Carsten Kjellerup, U.Ulla Weinreich, P.Philippe Bonniaud, O.Olivier Brun, P.Pierre‐régis Burgel, C.Christos Chouaid, F.Francis Couturaud, J.Jacques de Blic, D.Didier Debieuvre, D.Dominique Delsart, A.Axelle Demaegdt, P.Pascal Demoly, A.Antoine Deschildre, G.Gilles Devouassoux, C.Carole Egron, L.Lionel Falchero, F.François Goupil, R.Romain Kessler, P.Pascal Le Roux, P.Pascal Mabire, G.Guillaume Mahay, S.Stéphanie Martinez, B.Boris Melloni, L.Laurent Moreau, C.Chantal Raherison, E.Emilie Riviere, P.Pauline Roux-Claudé, M.Michel Soulier, G.Guillaume Vignal, A.Azzedine Yaici, S. P.Sven Philip Aries, R.Robert Bals, E.Ekkehard Beck, A.Andreas Deimling, J.Jan Feimer, V.Vera Grimm-Sachs, G.Gesine Groth, F.Felix Herth, G.Gerhard Hoheisel, F.Frank Kanniess, T.Thomas Lienert, S.Silke Mronga, J.Jörg Reinhardt, C.Christian Schlenska, C.Christoph Stolpe, I.Ishak Teber, H.Hartmut Timmermann, T.Thomas Ulrich, P.Peter Velling, S.Sabina Wehgartner-Winkler, J.Juergen Welling, E.Ernst‐joachim Winkelmann, C.Carlo Barbetta, F.Fulvio Braido, V.Vittorio Cardaci, E. M.Enrico Maria Clini, M. T.Maria Teresa Costantino, G.Giuseppina Cuttitta, M.Mario Di Gioacchino, A.Alessandro Fois, M. P.Maria Pia Foschino-Barbaro, E.Enrico Gammeri, R.Riccardo Inchingolo, F.Federico Lavorini, A.Antonio Molino, E.Eleonora Nucera, V.Vincenzo Patella, A.Alberto Pesci, F.Fabio Ricciardolo, P.Paola Rogliani, R.Riccardo Sarzani, C.Carlo Vancheri, R.Rigoletta Vincenti, T.Takeo Endo, M.Masaki Fujita, Y.Yu Hara, T.Takahiko Horiguchi, K.Keita Hosoi, Y.Yumiko Ide, M.Minehiko Inomata, H.Hiromasa Inoue, K.Koji Inoue, S.Sumito Inoue, M.Motokazu Kato, M.Masayuki Kawasaki, T.Tomotaka Kawayama, T.Toshiyuki Kita, K.Kanako Kobayashi, H.Hiroshi Koto, K.Koichi Nishi, J.Junpei Saito, Y.Yasuo Shimizu, T.Toshihiro Shirai, N.Naruhiko Sugihara, K.Ken‐ichi Takahashi, H.Hiroyuki Tashimo, K.Keisuke Tomii, T.Takashi Yamada, M.Masaru Yanai, R. C.Ruth Cerino Javier, A. D.Alfredo Domínguez Peregrina, M. F.Marco Fernández Corzo, E. M.Efraín Montano Gonzalez, A.Alejandra Ramírez-Venegas, A.Adrian Rendon, W.Willem Boersma, R.R. Djamin, M.Michiel Eijsvogel, F.Frits Franssen, M.Martijn Goosens, L.Lidwien Graat-Verboom, J.Johannes Veen, R.Rob Janssen, K.Kim Kuppens, M.Mario van de Ven, O. P.Ole Petter Brunstad, G.Gunnar Einvik, K. J.Kristian Jong Høines, A.Alamdar Khusrawi, T.Torbjorn Oien, Y.Yoon‐seok Chang, Y. J.Young Joo Cho, Y. I.Yong Il Hwang, W. J.Woo Jin Kim, Y.Young‐il Koh, B.Byung‐jae Lee, K.Kwan‐ho Lee, S.Sang‐pyo Lee, Y. C.Yong Chul Lee, S. Y.Seong Yong Lim, K. H.Kyung Hun Min, Y.Yeon‐mok Oh, C.Choon‐sik Park, H.Hae‐sim Park, H.Heung‐woo Park, C. K.Chin Kook Rhee, H. J.Ho Joo Yoon, H.Hyoung‐kyu Yoon, A. A.Alvar Agusti García-Navarro, R.Rubén Andújar, L.Laura Anoro, M. B.María Buendía García, P. C.Paloma Campo Mozo, S.Sergio Campos, F. C.Francisco Casas Maldonado, M. C.Manuel Castilla Martínez, C. C.Carolina Cisneros Serrano, L. C.Lorena Comeche Casanova, D.Dolores Corbacho, F. d.Felix del Campo Matías, J.Jose Echave-Sustaeta, G. F.Gloria Francisco Corral, P. G.Pedro Gamboa Setién, M. G.Marta García Clemente, I. G.Ignacio García Núñez, J. G.Jose García Robaina, M. G.Mercedes García Salmones, J. M.Jose Maria Marín Trigo, M. N.Marta Nuñez Fernandez, S. N.Sara Nuñez Palomo, J. O.José Olaguibel Rivera, L. P.Luis Pérez de Llano, A. P.Ana Pueyo Bastida, A.Ana Rañó, J. R.José Rodríguez González-Moro, A. R.Albert Roger Reig, J. V.José Velasco Garrido, D.Dan Curiac, C.Christer Janson, C.Cornelia Lif-Tiberg, A.Anders Luts, L.Lennart Råhlen, S.Stefan Rustscheff, F.Frances Adams, D.Drew Bradman, E.Emma Broughton, J.John Cosgrove, P.Patrick Flood-Page, L.Liz Fuller, T.Timothy Harrison, D.David Hartley, K.Keith Hattotuwa, G.Gareth Jones, K.Keir Lewis, L.Lorcan Mcgarvey, A.Alyn Morice, P.Preeti Pandya, M.Manish Patel, D.David Price, K.Kay Roy, R.Ramamurthy Sathyamurthy, S.Swaminathan Thiagarajan, A.Alice Turner, J.Jørgen Vestbo, W.Wisia Wedzicha, T.Tom Wilkinson, P.Pete Wilson, L.Lo'Ay Al-Asadi, J.James Anholm, F.Frank Averill, S.Sandeep Bansal, A.Alan Baptist, C.Colin Campbell, M.Michael Campos, B.Bradley Chipps, G.Gretchen Crook, S.Samuel Deleon, A.Alain Eid, E.Ellen Epstein, S.Stephen Fritz, H.Hoadley Harris, M.Mitzie Hewitt, F.Fernando Holguin, G.Golda Hudes, R.Richard Jackson, A.Alan Kaufman, D.David Kaufman, A.Ari Klapholz, H.Harshavardhan Krishna, D.Daria Lee, R.Robert Lin, B.Barry Make, D.Diego Maselli-Caceres, V.Vinay Mehta, J.James Moy, U.Ugo Nwokoro, P.Purvi Parikh, S.Sudhir Parikh, F.Frank Perrino, J.James Ruhlmann, C.Catherine Sassoon, R.Russell Settipane, D.Daniel Sousa, P.Peruvemba Sriram and R.Richard Wachs. Treatable traits in the NOVELTY study.Respirology2711November 2022, 929-940
  • 5 articleD.Didier Bessis, S.Sophie Trouillet‐Assant, L.Léo‐Paul Secco, N.Nathalie Bardin, B.Brigitte Blanc, V.Véronique Blatière, C.Christine Chable‐Bessia, C.Christophe Delfour, C.Céline Girard, J.Jean‐Christophe Richard, N.Nathalie Gros, V.Vincent Le Moing, N.Nicolas Molinari, V.Valérie Pallure, A.Amandine Pisoni, N.Nadia Raison‐Peyron, E.Elisa Reynaud, É.Émilie Schwob, R.Rémi Pescarmona, Q.Quentin Samaran, M.Marjolaine Willems, T.Thierry Vincent, M. T.Mircea T Sofonea, A.Alexandre Belot and É.Édouard Tuaillon. COVID-19 pandemic-associated chilblains: more links for SARS-CoV-2 and less evidence for high interferon type I systemic response.British Journal of Dermatology1876December 2022, 1032-1035
  • 6 articleA.Alice Blet, J.Joel Mcneil, J.Julie Josse, B.Bernard Cholley, R.Raphaël Cinotti, G.Gad Cotter, A.Agnès Dauvergne, B.Beth Davison, K.Kévin Duarte, J.Jacques Duranteau, M.-C.Marie-Céline Fournier, E.Etienne Gayat, S.Samir Jaber, S.Sigismond Lasocki, T.Thomas Merkling, K.Katell Peoc’h, I.Imke Mayer, M.Malha Sadoune, P.-F.Pierre-François Laterre, R.Romain Sonneville, L.Lorraine Ware, A.Alexandre Mebazaa and A.Antoine Kimmoun. Association between in-ICU red blood cells transfusion and 1-year mortality in ICU survivors.Critical Care261December 2022, 307
  • 7 articleA.Arnaud Bourdin, S.Sébastien Bommart, G.Gregory Marin, I.Isabelle Vachier, A. S.Anne Sophie Gamez, E.Engi Ahmed, C.Carey Suehs and N.Nicolas Molinari. Obesity in women with asthma: baseline disadvantage plus greater small‐airway responsiveness.Allergy2022
  • 8 articleD.D. Caimmi and P.P Demoly. A review of allergen immunotherapy in asthma.Allergy and Asthma Proceedings434July 2022, 310-313
  • 9 articleB.Barbara Cardoso, T.Teresa Moscoso, M.Mário Morais-Almeida, P.Pascal Demoly and L.Luciana Tanno. Economic burden of drug-induced anaphylaxis: what can we do better?Current Opinion in Allergy and Clinical Immunology2242022, 234-241
  • 10 articleA. M.Anca Mirela Chiriac, R.Rongfei Zhu, L.Lisa Izquierdo, N.Nicolas Molinari and P.P Demoly. The minimal important difference of the allergic rhinitis control test.The Journal of Allergy and Clinical Immunology: In Practice107July 2022, 1924-1926.e2
  • 11 articleB.Bénédicte Colnet, J.Julie Josse, G.Gaël Varoquaux and E.Erwan Scornet. Causal effect on a target population: a sensitivity analysis to handle missing covariates.Journal of Causal Inference101September 2022, 372-414
  • 12 articleE.Eloi Debourdeau, G.Gabriel Planells, C.Chloe Chamard, D.David Touboul, M.Max Villain, P.Pascal Demoly, F.Fanny Babeau, P.Pierre Fournie and V.Vincent Daien. New Keratoconus Risk Factors: A Cross-Sectional Case—Control Study.Journal of Ophthalmology2022September 2022, 6605771
  • 13 articleP.Pascal Demoly, C.Christophe Leroyer, E.Elie Serrano, A.Annelore Le Maux, G.Gabrielle Magnier and A.Antoine Chartier. The SQ HDM SLIT-Tablet is safe and well tolerated in patients with House Dust Mite allergic rhinitis with or without asthma: A "real-life" French study.Clinical and Translational Allergy1232022, e12129
  • 14 articleP.Pascaline Descloux, C.Claire Boyer, J.Julie Josse, A.Aude Sportisse and S.Sylvain Sardy. Robust Lasso-Zero for sparse corruption and model selection with missing covariates.Scandinavian Journal of Statistics2022
  • 15 articleA.Alain Desgagné, P.Pierre Lafaye de Micheaux and F.Frédéric Ouimet. Goodness-of-fit tests for Laplace, Gaussian and exponential power distributions based on λ -th power skewness and kurtosis.StatisticsNovember 2022, 1-29
  • 16 articleA.Anouchka Fillard, A.Amelia Licari, N.Nicolas Molinari, G.Gianluigi Marseglia, P.Pascal Demoly and D.Davide Caimmi. Sensitivity of FEV1 and Clinical Parameters in Children With a Suspected Asthma Diagnosis.The Journal of Allergy and Clinical Immunology: In Practice111January 2023, 238-247
  • 17 articleT.Thierry Garaix, S.Stéphane Gaubert, J.Julie Josse, N.Nicolas Vayatis and A.Amandine Véber. Decision-making tools for healthcare structures in times of pandemic.Anaesthesia Critical Care & Pain Medicine412March 2022, 101052
  • 18 articleT.Tobias Gauss, J.Justin Richards, C.Costanza Tortù, F.-X.François-Xavier Ageron, S.Sophie Hamada, J.Julie Josse, F.François Husson, A.Anatole Harrois, T.Thomas Scalea, V.Valentin Vivant, E.Eric Meaudre, J.Jonathan Morrison, S.Samue Galvagno, P.Pierre Bouzat, F.François Albasini, O.Olivier Briot, L.Laurent Chaboud, S.Sophie Chateigner Coelsch, A.Alexandre Chaumat, T.Thomas Comlar, O.Olivier Debas, G.Guillaume Debaty, E.Emmanuelle Dupré-Nalet, S.Samuel Gay, E.Edouard Ginestie, E.Edouard Girard, J.Jules Grèze, M.Marc Haesevoets, M.Marie Hallain, E.Etienne Haller, C.Christophe Hoareau, B.Bernard Lanaspre, S.Safia Lespinasse, A.Albrice Levrat, R.Romain Mermillod-Blondin, P.Philippe Nicoud, E.Elisabeth Rancurel, J.-M.Jean-Marc Thouret, C.Claire Vallenet, C.Cécile Vallot, B.Bénédicte Zerr, P.-S.Paér-Sélim Abback, G.Gérard Audibert, M.Mathieu Boutonnet, T.Thomas Clavier, F.Fabrice Cook, M.Mohamed Eljamri, T.Thierry Floch, E.Elisabeth Gaertner, D.Delphine Garrigue, T.Thomas Geeraerts, S.Sebastien Gette, A.Anne Godier, P.Pierre Gosset, J.-L.Jean-Luc Hanouz, J.-D.Jean-Denis Moyer, O.Olivier Langeron, M.Marc Leone, J.Julien Pottecher, J.Jonathan Portaz, M.Mathieu Raux, M.Marion Scotto, M.Mathieu Willig and A.Alexia Hardy. Association of Early Norepinephrine Administration With 24-Hour Mortality Among Patients With Blunt Trauma and Hemorrhagic Shock.JAMA Network Open510October 2022, e2234258
  • 19 articleT.Thomas Godet, A.Audrey de Jong, C.Côme Garin, R.Renaud Guérin, B.Benjamin Rieu, L.Lucile Borao, B.Bruno Pereira, N.Nicolas Molinari, J.-E.Jean-Etienne Bazin, M.Matthieu Jabaudon, G.Gérald Chanques, E.Emmanuel Futier and S.Samir Jaber. Impact of Macintosh blade size on endotracheal intubation success in intensive care units: a retrospective multicenter observational MacSize-ICU study.Intensive Care Medicine2022
  • 20 articleC.Christophe Hédon, F.Frédéric Schnell, P.Philippe Sosner, F.Frédéric Chagué, I.Iris Schuster, M.Marc Julia, A.Alexandre Duparc, J.-M.Jean-Michel Guy, N.Nicolas Molinari, L.Lionel Michaux, F.Frédéric Cransac and S.Stéphane Cade. Cardiac screening before returning to elite sport after SARS-CoV-2 infection.Archives of cardiovascular diseases11511November 2022, 562-570
  • 21 articleL.L. Klimek, R.R. Brehler, R.R. Mösges, P.P. Demoly, J.J. Mullol, D.D. Wang, R.R O'Hehir, A.A. Didier, M.M. Kopp, C.C. Bos and E.E. Karagiannis. Update about Oralair® as a treatment for grass pollen allergic rhinitis.Human Vaccines & Immunotherapeutics1852022, 2066424
  • 22 articleC.Christina Krantz, S.Simone Accordini, K.Kjell Alving, A. G.Angelo Guido Corsico, P. M.Pascal M. Demoly, D. S.Diogenes Seraphim Ferreira, B.Bertil Forsberg, J.Judith Garcia-Aymerich, T.Thórarinn Gislason, J.Joachim Heinrich, R.Rain Jõgi, A.Ane Johannessen, B.Bénédicte Leynaert, A.Alessandro Marcon, J.Jesús Martínez-Moratalla Rovira, E.Elisabet Nerpin, D. A.Dennis Alexander Nowak, A. C.Anna Carin Olin, M.Mario Olivieri, A. R.Antonio R. Pereira-Vega, C.Chantal Raherison-Semjen, F.Francisco Real, T.Torben Sigsgaard, G.Giulia Squillacioti, C.Christer Janson and A.Andreï Malinovschi. Cross-sectional study on exhaled nitric oxide in relation to upper airway inflammatory disorders with regard to asthma and perennial sensitization.Clinical and Experimental Allergy5222022, 297-311
  • 23 articleE.Eric Macy, A.Axel Trautmann, A. M.Anca Mirela Chiriac, P.Pascal Demoly and E.Elizabeth Phillips. Advances in the Understanding of Drug Hypersensitivity: 2012 Through 2022.The Journal of Allergy and Clinical Immunology: In Practice111January 2023, 80-91
  • 24 articleI.Imke Mayer, A.Aude Sportisse, N.Nicholas Tierney, N.Nathalie Vialaneix and J.Julie Josse. R-miss-tastic: a unified platform for missing values methods and workflows.The R JournalJuly 2022
  • 25 articleL.Lili Merliot-Gailhoustet, C.Chloé Raimbert, O.Océane Garnier, J.Julie Carr, A.Audrey de Jong, N.Nicolas Molinari, S.Samir Jaber and G.Gerald Chanques. Discomfort improvement for critically ill patients using electronic relaxation devices: results of the cross-over randomized controlled trial E-CHOISIR (Electronic-CHOIce of a System for Intensive care Relaxation).Critical Care261December 2022, 263
  • 26 articleA.Alexandre Perez-Lebel, G.Gaël Varoquaux, M.Marine Le Morvan, J.Julie Josse and J.-B.Jean-Baptiste Poline. Benchmarking missing-values approaches for predictive models on health databases.GigaScience2022
  • 27 articleC.Chloé Raimbert, O.Océane Garnier, J.Julie Carr, A.Audrey de Jong, N.Nicolas Molinari, S.Samir Jaber and G.Gerald Chanques. Publisher Correction to: Discomfort improvement for critically ill patients using electronic relaxation devices: results of the cross‑over randomized controlled trial E‑CHOISIR (Electronic‑CHOIce of a System for Intensive care Relaxation).Critical Care261December 2022, 291
  • 28 articleN.Nicolas Roche, G.Gilles Garcia, A.Alexandre de Larrard, C.Charlotte Cancalon, S.Stève Bénard, V.Vincent Perez, A.Aymeric Mahieu, L.Laurine Vieu and P.Pascal Demoly. Real-life impact of uncontrolled severe asthma on mortality and healthcare use in adolescents and adults: findings from the retrospective, observational RESONANCE study in France.BMJ Open128August 2022, e060160
  • 29 articleA.Amélie Rolle, A.Audrey de Jong, E.Elsa Vidal, N.Nicolas Molinari, E.Elie Azoulay and S.Samir Jaber. Cardiac arrest and complications during non-invasive ventilation: a systematic review and meta-analysis with meta-regression.Intensive Care Medicine2022
  • 30 articleF.François Roubille, E.Eric Matzner-Lober, S.Sylvain Aguilhon, M.Max Rene, L.Laurent Lecourt, M.Michel Galinier, J.Jean‐etienne Ricci and N.Nicolas Molinari. Impact of global warming on weight in patients with heart failure during the 2019 heatwave in France.ESC Heart Failure2022
  • 31 articleD.Dermot Ryan, B.Bertine Flokstra-de Blok, E.Evangéline Clark, C.Clara Gaudin, M.Myriam Mamodaly, J.Janwillem Kocks, J. L.Jantina Lucia van Der Velde, L.Liz Angier, K.Kerstin Romberg, R.Radek Gawlik, P.Pascal Demoly and L. K.Luciana Kase Tanno. Allergic and hypersensitivity conditions in non-specialist care: Flow diagrams to support clinical practice.Allergy2022
  • 32 articleQ.Quentin Samaran, R.Romain Samaran, E.Ernestine Ferreira, N.Naeda Haddad, A.Antoine Fottorino, H.Hervé Maillard, B.Brigitte Dreno, N.Nicolas Meyer, D.David Azria, E.Eve Maubec, C.Caroline Gaudy-Marqueste, N.Nicolas Molinari, P.-E.Pierre-Emmanuel Stoebner and O.Olivier Dereure. Anti-PD-1 for the treatment of advanced cutaneous squamous cell carcinoma in elderly patients: a French multicenter retrospective survey.Journal of Cancer Research and Clinical OncologyAugust 2022, Online ahead of print
  • 33 articleE. S.Erla S. Sigurðardóttir, T.Thórarinn Gislason, B.Bryndís Benediktsdottir, S.Steinar Hustad, P.P. Dadvand, P.P Demoly, K. A.Karl Anders Franklin, J.Joachim Heinrich, M.Mathias Holm, D. A.Diana A. van der Plaat, R.Rain Jõgi, B.Bénédicte Leynaert, E. B.Eva Boström Lindberg, J.Jesús Martinez-Moratalla, L. S.Leire Sainz de Aja, G.Giancarlo Pesce, I.Isabelle Pin, C.Chantal Raherison, A. R.Antonio R. Pereira-Vega, F.Francisco Real and K.Kai Triebner. Female sex hormones and symptoms of obstructive sleep apnea in European women of a population-based cohort.PLoS ONE176 June2022
  • 34 articleW.Witchaya Srisuwatchari, C.Charles Tacquard, A.Anna Borushko, S.Simon Viville, R.Rodolphe Stenger, Y.Yoann Ehrhard, J.-M.Jean-Marc Malinovsky, P.-M.Paul-Michel Mertes, P.Pascal Demoly and A. M.Anca Mirela Chiriac. Diagnostic performance of serial serum total tryptase measurement to differentiate positive from negative allergy testing among patients with suspected perioperative hypersensitivity.Clinical and Experimental Allergy522February 2022, 334-344
  • 35 articleW.Witchaya Srisuwatchari, T.Tram Vo, A.Amélie Gauthier, N.Nicolas Molinari, R.Rik Schrijvers, P.Pascal Demoly and A. M.Anca Mirela Chiriac. Hypersensitivity reactions to iodinated radiocontrast media: Cluster analysis reveals distinct clinical phenotypes.World Allergy Organization Journal1592022, 100680
  • 36 articleL. K.Luciana Kase Tanno and P.Pascal Demoly. Food allergy in the World Health Organization's International Classification of Diseases (ICD)‐11.Pediatric Allergy and Immunology3311November 2022, e13882
  • 37 articleL.Lk Tanno, A.Amélie Gauthier, S.Salome Allichon and P.P Demoly. Global patterns of drug allergy-induced fatalities: a wake-up call to prevent avoidable deaths.Current Opinion in Allergy and Clinical Immunology2242022, 215-220
  • 38 articleL. K.Luciana Kase Tanno, N.Nicolas Molinari, I.Isabella Annesi‐maesano, P.Pascal Demoly and A. L.Ana Luiza Bierrenbach. Anaphylaxis in Brazil between 2011 and 2019.Clinical and Experimental Allergy52July 2022, 1071 - 1078
  • 39 articleJ.Joana Vitte, M.Moïse Michel, A.Andrei Malinovschi, M.Marco Caminati, A.Adeyinka Odebode, I.Isabella Annesi‐maesano, D. P.Davide Paolo Caimmi, C.Carole Cassagne, P.Pascal Demoly, E.Enrico Heffler, E.Estelle Menu, B. I.Bright I Nwaru, Y.Youssouf Sereme, S.Stéphane Ranque, M.Monika Raulf, W.Wojciech Feleszko, C.Christer Janson, C.Carmen Galán and E. T.Eaaci Task Force On Allergic Bronchopulmonary Aspergillosis. Fungal exposome, human health, and unmet needs: A 2022 update with special focus on allergy.Allergy7711November 2022, 3199-3216
  • 40 articleM.Mathilde Volpato, J.Jerome Vialaret, C.Christophe Hirtz, A.Aurélie Petit, C.Carey Suehs, J.Jérémy Patarin, E.Eric Matzner-Lober, I.Isabelle Vachier, N.Nicolas Molinari, A.Arnaud Bourdin and J.Jeremy Charriot. Rheology predicts sputum eosinophilia in patients with muco-obstructive lung diseases.Biochemical and Biophysical Research Communications6222022, 64-71

Edition (books, proceedings, special issue of a journal)

  • 41 proceedingsM.Margaux ZaffranO.Olivier FéronY.Yannig GoudeJ.Julie JosseA.Aymeric DieuleveutAdaptive Conformal Predictions for Time Series.2022

Reports & preprints

Other scientific publications

  • 44 articleM.Moises Calderon and P.P Demoly. Integrating the evidence in allergen immunotherapy: Why real-world data should be important for the practicing clinician?Allergy and Asthma Proceedings434July 2022, 305-309

12.3 Cited publications

  • 45 bookN.Nicolo Cesa-Bianchi and G.Gábor Lugosi. Prediction, learning, and games.Cambridge university press2006
  • 46 articleI.Isaac Gibbs and E.Emmanuel Candès. Adaptive Conformal Inference Under Distribution Shift.arXiv:2106.00170 [stat]arXiv: 2106.00170June 2021, URL: http://arxiv.org/abs/2106.00170
  • 47 articleJ.Jing Lei, M.Max G'Sell, A.Alessandro Rinaldo, R. J.Ryan J. Tibshirani and L.Larry Wasserman. Distribution-Free Predictive Inference for Regression.Journal of the American Statistical Association1135232018, 1094--1111
  • 48 inproceedingsH.Harris Papadopoulos, K.Kostas Proedrou, V.Volodya Vovk and A.Alex Gammerman. Inductive Confidence Machines for Regression.Machine Learning: ECML 2002Springer2002, 345--356
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